Abstract: We use new manufacturing GDP time series to examine the industrialization in Argentina, Brazil, Chile, and Colombia since the early twentieth century. We uncover variation across countries and over time that the literature on industrialization had overlooked. Rather than providing a single explanation of how specific shocks or policies shaped the industrialization of the region, our argument is that the timing of the industrial take off was linked to initial conditions, while external shocks and macroeconomic and trade policy explain the variation in the rates of industrialization after the 1930s and favorable terms of trade and liberalization explain deindustrialization after 1990.

The long road of protectionism in Latin America in the decades between 1930 and 1990 led not only to import substitution of goods, but also of ideas. During those decades each country thought its way of development distanced from its neighbors, despite relatively similar schools of thought under the care of the Economic Commission for Latin America and the Caribbean (ECLAC). The result was a myriad of studies focused on peculiarities – what made each country unique in its backwardness – largely ignoring the possibility of comparative perspectives. Of course, comparative studies existed, but the view of Latin America as an object of study until the 1980s was delegated to a secondary place, shared more by international agencies and foreign researchers who sought a more macro understanding of the region.

During the last three decades things changed, but we still feel the effects of these“lost decades”. “Intellectual isolation” was especially true in Brazil, which until today has very few university courses on the economic history of other Latin American countries. The paper of Gerardo Paolera, Xavier Durán, and Aldo Musacchio, therefore, is a much welcome attempt to understand the differences in long-term development in South America using comparative data for Argentina, Brazil, Chile, and Colombia. They present a history of industrialization in these countries putting together series of manufacturing value added, labor productivity in manufacturing, the size of the labor force, and trade series for the whole twentieth century (until 2010, actually). Despite arguing that they estimated new figures when the data was not available, the authors mostly use secondary sources for macroeconomic data (for example, Brazil’s data comes from IPEA, a government agency).

The paper’s main argument is that the long-term series of industrial GDP suggest that the patterns of industrialization in those countries were heterogenous, and initial conditions – such as level of urbanization, literacy and infrastructure development at the end of the 19th century – mattered more for the timing of industrial takeoff than policies or external shocks. Therefore, the authors reject traditional hypotheses that have tried to explain the industrialization of South America using “one single theory”. Among these traditional explanations are the “adverse shocks” hypothesis, industrialization as a product of export-led growth, and industrialization as the product of import substitution industrialization (ISI). The paper then proceeds to explain the differences between the four countries during the following periods: 1) before 1920, 2) the 1920s, 3) the Great Depression, 4) World War II, 5) the 1980s, 6) 1990s and beyond.

According to the paper, the long-term industrial series show that “none of these hypotheses explain all cases for the entire century.” Moreover, changes in external conditions and domestic policies explain part of the variation in the rates of industrialization only after the 1930s. In their review about the different periods of industrialization, the highlight is for the effects of ISI policies on industrialization. They present a “real distorted import price” index – which are import prices multiplied by the average tariff and the nominal exchange rate – to show the correlation between price distortion of imports and growth of manufacturing as a percentage of GDP. This correlation is widely known in the historical literature, but bringing together data for the South American countries helps us to understand the relative size of barriers to trade in each country.

Paolera, Duran, and Musacchio’s paper is an interesting contribution, however, it is not clear how much of it is a revisionist interpretation of South America’s industrialization. It would be interesting to have a better sense about how much the literature on Latin America industrialization in the twentieth century really argues that the process was homogeneous across countries and that domestic and initial conditions did not matter. Even in books that summarize the literature, such as Bértola and Ocampo (2012) there are clear differences between the countries and initial conditions (their Human Development Index for example).

As a side note, it also feels unnecessary to argue that the countries shared similar culture, religion, and colonial origin to “control” for cross-sectional variation. Is there really a relevant connection between these conditions and different periods and types of industrialization? Besides the fact that many Argentineans, Brazilians, and Chileans will try to “argue” that they have a very different culture (and, in the case of Brazil, colonial origin), it would be good to show if the traditional hypotheses make these connections.

Moreover, since initial conditions (human capital) mattered for industrialization, why is East Asia a proper counterfactual for Latin America? The authors argue that we “need to improve our knowledge” on this issue, but it feels there is room to present more recent research about the topic, not only Robert Wade’s (1990) book: in the style of Liu (2017) and Lane (2017). Also, as a suggestion, it would be interesting to see the index for “real distorted import prices” for East Asian countries, as it would teach us something about Latin America.

The 1980s and 1990s could also have a more extensive literature review. For example, the paper argues that the improvement in terms of trade after the 1990s was associated with “some form of Dutch Disease”. However, there is not sufficient evidence to make this statement. Their measure of de-industrialization, which is a declining share of manufacturing in total GDP, is a limited way to measure de-industrialization, especially when productivity of the other sectors (like agriculture) was increasing. The lower share of manufacturing after the 1980s could also be a form of correction after the excesses of the 1960s and 1970s. Indeed, we still do not have a clear answer about the opportunity cost of those policies. Nevertheless, the Brazilian’s government attempt (and failure) to resuscitate the policies of the military regime in the years after 2008 shows us that the cost-benefit of industrialization at any cost in previous decades needs to be re-evaluated (as they were in Musacchio and Lazzarini 2014). After three decades of declining knowledge barriers between South American countries, perhaps it is time to “demand” the next step in historical comparative studies: micro studies.

References

Bertolá, Luis and José Antonio Ocampo’s The Economic Development of Latin America since Independence. Oxford: Oxford University Press, 2012.

Abstract:How does democracy emerge from authoritarian rule? Influential theories contend that incumbents deliberately choose to share or surrender power. They do so to prevent revolution, motivate citizens to fight wars, incentivize governments to provide public goods, outbid elite rivals, or limit factional violence. Examining the history of all democratizations since 1800, I show that such deliberate choice arguments may help explain up to one third of cases. In about two thirds, democratization occurred not because incumbent elites chose it but because, in trying to prevent it, they made mistakes that weakened their hold on power. Common mistakes include: calling elections or starting military conflicts, only to lose them; ignoring popular unrest and being overthrown; initiating limited reforms that get out of hand; and selecting a covert democrat as leader. These mistakes reflect well-known cognitive biases such as overconfidence and the illusion of control.

In his paper “Democracy by Mistake,” Daniel Treisman attempts to provide a new answer to the important question of how democracy emerges from authoritarian rule. Different from previous theories, which attribute the fall of non-democratic regimes to calculated decisions by those in power, Treisman argues that most episodes of democratization happened because dictators – like everyone else – are in fact bad at probabilities. Like all humans, but especially so given their circumstances, dictators are prone to overconfidence and the illusion of control. Living the life of confirmation bias, therefore, non-democratic regimes start to lose their grip on power making avoidable mistakes. Mistakes, of course, are only truly avoidable in hindsight, but the strength in Treisman’s paper relies on his argument that two thirds of his recorded cases of democratization do not fit the usual interpretation that dictators deliberately choose to share or surrender power when democracies start to emerge.

In the democratization by choice category, Treisman presents six common arguments from the literature and divides them into three schools of thought – democracy by bargain, splits within ruling circles, and democracy as a peace-making device. The general argument, nonetheless, is that intentional democratization happens when the ruler weights his probabilities and concludes that if he does not reduce his personal power in the short run, he will have worse problems in the long run – e.g., the dictator will have no money to fight wars or will have people with torches at his front door.

In the democratization by “bad choice” category, the mistakes that lead to democratization are, in a simplified way, the following: ignoring warnings and getting overthrown by popular revolt; calling a referendum or election— and losing; initiating or entering a military conflict— and losing; enacting partial reforms to stabilize the regime— but undermining it; selecting a leader to preserve the regime— who destroys it; and using repression counterproductively.

To divide cases of democratization between intentional and non-intentional, Treisman analyses 218 episodes since 1800 using two definitions of democracy. The first one is a binary concept, called “qualitative,” which uses data from Boix, Miller, and Rosato (2013). In their definition, democracy exists in a country when elections are free and competitive, the head of government is either directly elected or answerable to an elected parliament, and at least half the male population has the right to vote. The second definition of democratization is called “directional”, and it uses the Polity IV database, which measures countries regimes on a 21-point scale – from -10 for hereditary monarchies to +10 in the case of consolidated democracies. For a country to be considered a democracy, the Polity IV establishes that it must have a score of at least 6.

Some people just can’t let it go

With these categories, how can we interpret the empirical results of the paper? Is it the case that most episodes of democratization happened because dictators, overconfident in their position, made critical mistakes that ultimately undermined their power? The answer, it seems, depends a lot on what the author considers as a mistake. For example, among the episodes of intentional democratization, the end of the military dictatorship in Brazil in 1985 is categorized as a “great compromise.” I agree that this is a good example of intentional democratization due to political compromise, however, one could argue the opposite using the framework presented in the paper. It is known in Brazilian political history that the last military president in Brazil did not begin his term planning to be the last. In fact, his indecision in choosing/supporting a successor led to the strengthening of civil groups demanding increased access to the government (Dimenstein 1985). Therefore, the democratization of Brazil after 1985 also started with a dictator who was overconfident he could maintain the status quo (and who also overestimated his own relative competence). However, is this sufficient to defend the hypothesis that democracy was the outcome of individual mistakes? What about the institutional environment that allowed such significant transformations to happen? It seems that “critical mistakes that undermine power” cannot be restricted to the cognitive biases of “great men.” The fact that democratic reforms are not intentional does not necessarily lead us to the “democracy by mistake” camp.

Another issue of using multiple categories is that sometimes it is not clear which examples are really being used as a mistake. Take the “initiating or entering a military conflict – and losing” category, which account for 6-9 percent of democratization processes, according to the paper. Among the examples is the well-known miscalculated attack by the Argentine military government on the Falkland Islands in 1982. However, Treisman also uses as an example for this category Paraguay’s attack on Brazil in 1864, which started the War of the Triple Alliance. It is strange that these two episodes are bundled together because the death of Solano López (the Paraguayan dictator) was not followed by a democratic government. In fact, the data that Treisman uses also don’t assign this period to a democratic transition: Polity IV only assigns democracy to Paraguay in 1994, and Boix et al. data indicates that Paraguay became democratic in 2003. Moreover, one must not forget that Solano attacked Brazilian officials right after Brazil deposed his allies in Uruguay and potentially ended his access to important continental rivers and the sea. This could be considered a critical mistake, but I couldn’t understand if the author is considering any mistake that weakens authoritarian rule as a valid example, or if his examples are only for when the mistakes turn into a democracy. Does Solano’s choice count as a “leap in the dark” to democracy or not?

Policy IV authority trend for Brazil (with comments)

Treisman asserts that intentionalist theories find weak support in historical cases, but using behavioral science as a mechanism to explain democratic transitions seems insufficient to explain transformations that usually are larger than individuals. Treisman makes arguments such as: “neurological evidence suggests power can impair the ability to process the actions and emotions of others” (p. 28), and “physical and mental deterioration affect[ing] leaders in all systems, they are more likely to impair decision making in autocracies.” (p. 29). Nonetheless, it is surprising that even today dictatorships seem stable in the eyes of those outside it – until the day they are no more. Angola and Zimbabwe are recent examples of this. It seems that there is always the illusion of control, even if dictators stay in power. Isn’t it the case that our mistakes inevitably turn into naive memories that, if not for one detail, make us think that everything could have been different?

Abstract – 1993 Nobel laureates Robert Fogel and Douglass North were pioneers in the “new” economic history, or cliometrics. Their impact on the economic history discipline is great, though not without its critics. In this essay, we use both the “old” narrative form of economic history, and the “new” cliometric form, to analyze the impact each had on the evolution of economic history.

Douglass North and Robert Fogel’s contribution to the rise of the “new” economic history is well known, but Diebolt and Haupert’s paper adds a quantitative twist to their roles as active supporters of cliometrics when there was still resistance to apply new methods to the study of the past. Economic theory and formal modeling marked the division between the “old” and the “new” economic historians in the 1960s, and Diebolt and Haupert use two metrics to track the transformation in the field: 1) the increased use of graphs, tables, and especially equations during North’s period as editor (along with William Parker) of the Journal of Economic History between 1961 and 1966; 2) the citation of Fogel’s railroad work, to measure the impact of his innovations in economic history methodology.

Before showing their results about the positive influence of North and Fogel on quantitative economic history, the authors present a brief history of cliometrics, beginning with the 1957 meeting of the Economic History Association (EHA). It was there that Alfred Conrad and John Meyer presented their two foundational papers, about the use of economic theory and statistical inference in economic history, and the economics of slavery in the antebellum South. From that meeting, William Parker edited what was probably the first book (released in 1960) of the cliometric movement.

It was during the 1960s, however, that larger changes would occur. First, Parker and North were appointed editors of the Journal of Economic History (JEH) in 1961 and began to promote papers that used more economic theory and mathematical modelling. Their impact appears in Figures 2 and 3, which show a measure of “equations per page” and “graphs, tables, and equations per page” in the JEH since its first issue in 1941.

As a way stay true to the spirit of the discussion, Diebolt and Haupert test the hypothesis if the period between 1961 and 1966 had an enduring effect in the increase of “math” in the JEH. Despite a noticeable increase in the North and Parker years, it was only in 1970 that a significant “level shift” occurs in the series, and Diebolt and Haupert argue that this could be interpret as a lag effect from the 1961-1966 period. Their finding that 1970 marks a shift in the methodology of papers published in the JEH is consistent with the overall use of the word cliometrics in other publications, as a NGRAM search shows.

In addition to the editorial impact of Douglass North in the JEH, the second wave of change in economic history during the 1960s was Robert Fogel. In 1962, Fogel published his paper about the impact of railroads in American economic growth. The conclusion that railroads were not essential to America, along with the use of counterfactuals to arrive at that result, “attracted the attention of the young and the anger of the old” economic historians (McCloskey, 1985, p. 2). Leaving the long debate about counterfactuals aside, what Fogel’s work showed was that the economics methodology at the time was useful to overcome the limitations of interpreting history based only on what historical documents offered at face value.

Diebolt and Haupert’s paper, therefore, shows that cliometric research in the JEH had a positive exogenous shock with North as an editor, with Fogel supplying the demand brought by the new editorial guidelines. However, there is a complementary narrative about these developments that deserves to be mentioned. Many innovations in methodology brought to the field after 1960 came from researchers who were primarily concerned with economic growth, not only with historical events. This idea appears in the paper, when the authors argue that during his post-graduate studies, the starting point of Fogel’s research was about the “large processes of economic growth” (p.8). In addition, the realization that Fogel’s training program “was unorthodox for an economic historian” is also indicative that, in the 1960s, with computational power and new databases that extended to the 19th century, history was the perfect case study to test economic theory.

This exogenous impact in the field, with clear beneficial results, is similar to the role Daron Acemoglu and his many authors had in reviving economic history in the last decade to a broader audience. Acemoglu initial focus when he presented a different way to do research in economic history was in the present (i.e. long-run growth), not the past. It seems, therefore, that the use of mathematical models in economic history was not a paradigm shift in the study of history, but rather it followed the change from what was considered “being an economist” in the United States. After 1945, Samuelson’s Foundations of Economic Analysis set the standard for the type of training that econ students received, turning mathematical models as the dominant method in economics (Fourcade, 2009, p. 84). Cliometrics, by following this trend, created an additional way to do research in economic history.

One comparative advantage of the new economic historians, in addition to the “modern” training in economics, was something that can be called the Simon Kuznets effect. Both North and Fogel worked with Kuznets, and the development of macroeconomic historical databases at the NBER after the 1930s provided the ground to apply new methodologies to understand economic growth. In the first edition of the Journal of Economic History Kuznets already advocated the use of statistical analysis in the study of history (Kuznets, 1941). But the increase in popularity of models and statistics in economic history, especially in the 1970s (see Temin, 2013), seems to be related to its impact to understand the broader questions of economics. One notable example comes with Milton Friedman and Anna Schwartz’s monetary history of the United States, published in 1966. Friedman worked with Kuznets in the 1930s, and the book is the typical research in economic history with a focus on “contemporary” issues.

As Diebolt and Haupert claim, North and Fogel contribution is undeniable, but what about the contrafactual they propose in the title? Just as no single innovation was vital for economic growth, probably no economic historian was a necessary condition for cliometrics. Without North and Fogel, maybe the old economic historians would have had another decade, but by the 1970s the JEH would be under new management.

References

Fourcade, M. (2009) Economists and Societies: Discipline and Profession in the United States, Britain, and France, 1890s to 1990s. Princeton, NJ: Princeton University Press.

The prevailing explanation for why the Industrial Revolution occurred first in Britain is Robert Allen’s (2009) ‘high-wage economy’ view, which claims that the high cost of labour relative to capital and fuel incentivized innovation and the adoption of new techniques. This paper presents new empirical evidence on hand spinning before the Industrial Revolution and demonstrates that there was no such ‘high-wage economy’ in spinning, a leading sector of industrialization. We quantify the working lives of frequently ignored female and child spinners who were crucial to the British textile industry in the Early Modern period with evidence of productivity and wages from the late sixteenth to the early nineteenth century. Our results show that spinning was a widespread, low-wage, low-productivity employment, in line with the Humphries (2013) view of the motivations for the factory system.

Review by Thales Zamberlan Pereira

In Spinning the Industrial Revolution, Humphries and Schneider last words are: “the route to mechanization and factory production was a response to low not high wages.” This is a direct statement against Robert Allen’s high wage economy (HWE) explanation for the Industrial Revolution. The low wage authors (LWA) argue that the wages Allen uses were only available to a “rare group of spinners” and, therefore, were not a representative sample, which should include lower wages from women and children. There was a direct link between productivity and remuneration, and only a limited number of spinners could produce several pounds of fiber in a week and/or had the ability to make finer counts of yarns.

Water Frame, about 1775

Humphries and Schneider present an important discussion about different sources for spinner’s wages and how we should measure their earnings, but what does their evidence mean for Allen’s HWE? I leave to Allen himself to respond: “Humphries never analyses the British labour supply from an international perspective.”[i] Even considering the lower wages from women and children in spinning, the important question is if real wages in Britain were higher than other parts of the world. The authors avoid this discussion, making the alternative argument that “there should have been an increase/jump” in spinners’ wages before the innovations period (around 1760s). But since Allen’s explanation for the Industrial Revolution has a “global perspective”, what matters is if wages in Britain (or in the northwest regions) were higher than in comparable regions in Europe (we can also add Asia here). Humphries and Weisdorf ‘s paper (“Unreal Wages”), along with many other recent research (Broadberry et al. latest book), shows that real wages were slowly increasing for centuries, so why there is a need for a spike? In addition, since inventions in spinning were largely associated with cotton, one important limitation of the paper is that most of the primary sources used for spinning productivity are not for cotton (See Table 4). As pointed out by John Styles, there is even no proper data for Lancashire, the main cotton region.

The LWA also make the argument that inventors (such as Arkwright) never expressed concern about high wages in spinning. But if spinners did not have wages higher than the British average, even if Britain had the highest wages in the world, one would not expect this demand. In the age before spinning machinery, when the “earnings in weaving were constrained” by low productivity, how much the average wages should be used to measure the connection between high costs and innovation? There are two aspects here that deserve some attention: higher wages for those workers with higher productivity, and a “wage premium” for those who produced finer yarns. Humphries and Schneider argue that, since spinners were paid piece rates, there was a demand for more “experienced spinners” to produce finer counts of yarn. As the long debate between Nick Harley and Javier Cuenca Esteban has showed, finer cotton textiles were the first wave of products that came out of the new inventions. The low productivity of a spinner to produce a 20-count yarn (a high count at the time), presented in the paper, suggests that to use averages wages to test its impact on innovation may be misleading in the case of textiles. The average spinner could not produce a yarn with the quality (and quantity) required to test the HWE hypothesis. This, I think, is part of the argument that John Styles makes when he writes about the “general tendency in much of the literature to think about spinning as if were a single activity – unskilled women’s work.”

Humphries and Schneider conclude that “overcoming the low productivity and inconsistent quality in spinning and taking advantage of low wages for spinners
and female and child workers more generally may have been the spur for tinkerers
and inventors in the late eighteenth-century textile industry.” While the first part of this sentence is an important one, it would be interesting to see more evidence on the latter part. The reason for this is that recent projects to reconstruct the famous spinning machines showed that they were “uncomfortable” to use and needed “a fair degree of strength to operate.”[ii] Since some of the locations for the author’s spinning records contain a large proportion of children, it would be useful to know if they really could operate the spinning machines.

The debate between the LWE and the HWE hypotheses prompted a series of very interesting replies during the last few weeks (see Judy Stephenson, Vincent Geloso, John Styles, Psedoerasmus). There are still a lot of questions to be answered, but maybe the next step for this debate to move forward is to have better real wages for France. New French real wages would present the “global perspective” that Humphries and Schneider’s paper lack. My take on this debate is that we should be conservative about what new pieces of evidence really mean for our broader interpretations of historical events. Otherwise we will just be jumping to the next omitted variable as the “real explanation.” The fact that the average wage for spinners was lower than the one presented by Allen does not imply that British high-wage economy was a statistical artifact. We need better data for other countries before claiming that “the route to mechanization and factory production was a response to low not high wages.”

Abstract: The objective of this article is to reappraise both the accuracy of the official export statistics and the narrative of Brazilian export growth during the period immediately following independence. We undertake an accuracy test of the official values of Brazilian export statistics and find evidence of considerable under-valuation. Once corrected, during the post-independence decades (1821-50) Brazil’s current exports represented a larger share of its economy and its constant growth is found to be more dynamic than any other period of the nineteenth century. We posit that this dynamism was related to an exogenous institutional shock in the form of British West Indies slave emancipation that afforded Brazil a competitive advantage.

Reviewed by Thales A. Zamberlan Pereira (University of São Paulo)

The best place to find the (rather scarce) macroeconomic data for 19th century Brazil are the official statistics compiled by the Brazilian Statistics Institute (IBGE). The IBGE data is the main source in Brian Mitchell’s international historical statistics and both are commonly used in the literature exploring Brazilian economic history. The paper by Absell and Tena is an attempt to test the accuracy of these sources by looking at official export statistics between 1821 and 1913. If nothing else this already makes this an interesting paper.

The focus in export data relies on the argument that the Brazilian economy remained stagnant during the decades that followed Brazil’s independence until 1850 when there was renewed economic growth. While the more recent literature suggests the development of a domestic economy before 1850, the more “classic” literature focuses on the foreign sector to calculate Brazil’s economic growth in the 19th century.

Absell and Tena confirm previous findings that official export statistics were undervaluing exports after 1850. But their study extends to the earlier period and suggests that official statistics also had a significant bias for the first half of the 19th century. In particular their analysis suggests that Brazilian export growth before 1850 was much higher than previously assumed and that a change in international demand, especially for coffee, was the principal determinant for this growth. The last section of the paper tries to explain the sources of Brazil’s “dynamic export growth” during the post-independence decades and shows that an increase in foreign demand was much more important than changes in domestic productivity. The high rate of growth in exports between 1821 and 1850, a very interesting result, is calculated by deflating prices using an index from a new series of commodities prices.

Comment

All of Absell and Tena’s results are grounded in the price correction of the official export data and, therefore, the most interesting part of the paper is the reconstruction of Brazil’s export statistics. To correct the official data, they used international prices for the different commodities (mainly cotton, sugar, and coffee) and subtract freight rates, insurance costs, and export taxes. That is, they convert c.i.f. (cost, insurance and freight) values to f.o.b. (free on board) creating new series for these variables. For insurance and freight rates they used trade data between Rio de Janeiro and Antwerp. It should be noted, however, that a large part of cotton exports before 1850 went to Britain, and freight rates between Brazil and Liverpool were half of what they were for freight travelling to Portugal or France.

Absell and Tena argue that official data for exports was sourced in a weekly table organized “by a government committee in consultation with local commodity brokers and commercial associations.” This information was then verified by the Ministry of Finance, who sent the tables to provincial customs houses (which calculated the tax revenue) and also to major news periodicals. If the official values were organized like this for the whole period under study, as the authors argue, it would be easier to doubt the accuracy of exports statistics. But, it is difficult to understand how a system of weekly information could work in a country the size of Brazil during the 19th century. Before 1850, northern provinces like Maranhão had stronger business relationships with Lisboa and Liverpool than with Rio de Janeiro. Some northern provinces did not support independence in 1822 because of close economic ties with Portugal.

An additional issue is that many important provinces, even after 1850, did not use the weekly table to calculate their taxes. Evidence suggests that in Minas Gerais and São Paulo, two major coffee exporters, the government used a fixed price system to calculate taxes. See, for example, debates at the provincial assembly of Rio de Janeiro, November 1862, 1879; available online. This information, of course, does not invalidate the argument about the inaccuracy of official values, but it provides some clues that the authors’ correction could have a significant bias as well.

Another problem with the transformation to f.o.b. prices regards export duties. In the working paper version of this article, they assume this “additional trade cost” represented between 1 to 7 per cent of export values. There is extensive evidence, however, that export taxes were a much higher burden throughout the 19th century. Debates at the Chamber of Deputies, the Senate, and in newspapers show that before the fiscal reform in the 1830s, export duties for sugar and cotton could reach more than 20 per cent. The export duties also varied across provinces. After 1850, they continued to be at least 10 per cent. The export duties presented by Absell and Tena are undervalued because their source from 1821 to 1869 only show the total revenue collected by the central government, not revenue collected by provincial custom-houses. Making assumptions in such calculations is valid, but information regarding data sources should have been more clearly explained in the published version.

Because the objective of the authors is to correct export values using more accurate price data, it should be clear that they do not use only price for Brazilian commodities to adjust the official statistics. To correct the value of Brazilian cotton exports, for example, they use price information of Guyana Raw (Berbice or Demerara) and Middling Uplands (United States) to the United Kingdom. The figure below shows the price of an arroba of cotton in pennies (d) from four different sources, including two prices series for Brazil not used in Absell and Tena paper. The first is the price from the official statistics (IBGE), the second is the price of cotton at the port of Maranhão, the third is the price of cotton from Maranhão in Liverpool, and the last one in the average price of West Indies in Liverpool. As can be seen, using prices for Brazilian cotton would change some of the magnitudes that the paper proposes.

In summary the paper by Absell and Tena makes a worthy contribution and it proposes a revisionist approach to an important source. An important problem in the paper, however, is not discussing how its own sources could limit their conclusions, a crucial aspect in any revisionist study.